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A HIGHLY PREDICTIVE MACHINE LEARNING MODEL TO IDENTIFY HOSPITALIZED PATIENTS AT RISK FOR 30-DAY READMISSION OR MORTALITY
The gradient boosted machine learning model was built on a large number of predictive features including the OM1 medical burden index (OM1 MBI), which is a standardized measure of the combined effect of current and prior conditions and treatments on current health status, generated from extensive an...
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Published in: | Journal of the American College of Cardiology 2018-03, Vol.71 (11), p.A1734-A1734 |
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Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | The gradient boosted machine learning model was built on a large number of predictive features including the OM1 medical burden index (OM1 MBI), which is a standardized measure of the combined effect of current and prior conditions and treatments on current health status, generated from extensive analysis of OM1's longitudinal patient cohort (n>175M). Conclusion Our model generated by advanced machine learning demonstrates superior performance to previously published predictive models, e.g., the established LACE+ index (C-statistic 0.76). |
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ISSN: | 0735-1097 1558-3597 |
DOI: | 10.1016/S0735-1097(18)32275-7 |